在升级tensorflow2.1之后，我得到了"RuntimeError: tf.placeholder()与渴望执行不兼容“。

import tensorflow as tf

# 定义数据集
data = [1, 2, 3, 4, 5]
dataset = tf.data.Dataset.from_tensor_slices(data)

# 对数据集进行预处理和批量读取
dataset = dataset.shuffle(5).batch(2)

# 构建迭代器
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()

# 在会话中执行
with tf.Session() as sess:
    sess.run(iterator.initializer)
    while True:
        try:
            print(sess.run(next_element))
        except tf.errors.OutOfRangeError:
            break

import tensorflow as tf

# 定义输入函数
def input_fn():
    data = [1, 2, 3, 4, 5]
    labels = [0, 1, 0, 1, 0]
    dataset = tf.data.Dataset.from_tensor_slices((data, labels))
    dataset = dataset.shuffle(5).batch(2)
    iterator = dataset.make_one_shot_iterator()
    features, labels = iterator.get_next()
    return features, labels

# 构建模型
def model_fn(features, labels, mode):
    # 模型定义
    ...

# 构建Estimator
estimator = tf.estimator.Estimator(model_fn=model_fn)

# 在Estimator中执行训练和评估
estimator.train(input_fn=input_fn, steps=1000)
estimator.evaluate(input_fn=input_fn, steps=100)